4.5 Article

Improved folded-PCA for efficient remote sensing hyperspectral image classification

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Environmental Sciences

A new method to detect targets in hyperspectral images based on principal component analysis

Shahram Sharifi Hashjin et al.

Summary: In this study, a new method is proposed to improve the performance of target detection algorithms based on principal component analysis (PCA) feature space. Experimental results demonstrate that this method achieves better performance in terms of false alarm rate compared to other methods.

GEOCARTO INTERNATIONAL (2022)

Article Environmental Sciences

PCA-based classification using airborne hyperspectral radiance data, a case study: Mount Horshan Mediterranean forest

Moshe Mandelmilch et al.

Summary: This study utilized radiance data and unsupervised principal component analysis-based classification to classify plant species, achieving an overall accuracy of 68% without the application of atmospheric correction or noise reduction.

GEOCARTO INTERNATIONAL (2022)

Article Environmental Sciences

Evaluation of aquifer vulnerability using PCA technique and various clustering methods

Bashir Rahmani et al.

Summary: The study initially utilized a clustering technique independent of ranks and weights, followed by employing Principal Component Analysis to select effective parameters, and ultimately prepared a vulnerability map of the Varamin Aquifer using the C-Means clustering method.

GEOCARTO INTERNATIONAL (2021)

Review Engineering, Electrical & Electronic

PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification

Md. Palash Uddin et al.

Summary: This study investigates various feature extraction methods including PCA and its linear (SPCA, SSPCA, FPCA, MNF) and nonlinear (KPCA, KECA) variants, using SVM classifier for classification of Indian Pine agricultural and Washington DC Mall HSI. Results demonstrate that feature extraction methods outperform using the entire dataset, with MNF achieving the highest classification accuracy and FPCA offering the least complexity with satisfactory classification results.

IETE TECHNICAL REVIEW (2021)

Article Remote Sensing

Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification

Md. Palash Uddin et al.

Summary: Hyperspectral images contain valuable information for land cover classification, with techniques like PCA for feature extraction and cumulative variance accumulation for feature selection commonly employed. However, non-linear measures like the nMI-based mRMR can be more effective at selecting intrinsic features from the transformed space of FE methods. Experiment results showed that these non-linear measures outperformed existing methods on real HSI datasets.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2021)

Article Remote Sensing

Effective feature extraction through segmentation-based folded-PCA for hyperspectral image classification

Md. Palash Uddin et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2019)

Article Remote Sensing

Land-cover classification using both hyperspectral and LiDAR data

Pedram Ghamisi et al.

INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION (2015)

Article Geography, Physical

Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

Jaime Zabalza et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2014)

Article Geochemistry & Geophysics

Kernel Entropy Component Analysis for Remote Sensing Image Clustering

Luis Gomez-Chova et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2012)

Article Computer Science, Artificial Intelligence

LIBSVM: A Library for Support Vector Machines

Chih-Chung Chang et al.

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2011)

Article Computer Science, Artificial Intelligence

Kernel Entropy Component Analysis

Robert Jenssen

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2010)

Article Environmental Sciences

Recent advances in techniques for hyperspectral image processing

Antonio Plaza et al.

REMOTE SENSING OF ENVIRONMENT (2009)

Article Remote Sensing

Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species

F. Tsai et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2007)

Article Geochemistry & Geophysics

Reducing the dimensionality of plant spectral databases

IE Bell et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2004)

Article Computer Science, Artificial Intelligence

Two-dimensional PCA: A new approach to appearance-based face representation and recognition

J Yang et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2004)

Article Computer Science, Artificial Intelligence

A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine

LJ Cao et al.

NEUROCOMPUTING (2003)

Review Computer Science, Artificial Intelligence

Statistical pattern recognition: A review

AK Jain et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2000)